Abstract
Drug development and biological discovery require effective strategies to map existing genetic associations to causal genes. To approach this problem, we selected 12 common diseases and quantitative traits for which highly powered genome-wide association studies (GWAS) were available. For each disease or trait, we systematically curated positive control gene sets from Mendelian forms of the disease and from targets of medicines used for disease treatment. We found that these positive control genes were highly enriched in proximity of GWAS-associated single-nucleotide variants (SNVs). We then performed quantitative assessment of the contribution of commonly used genomic features, including open chromatin maps, expression quantitative trait loci (eQTL), and chromatin conformation data. Using these features, we trained and validated an Effector Index (Ei), to map target genes for these 12 common diseases and traits. Ei demonstrated high predictive performance, both with cross-validation on the training set, and an independently derived set for type 2 diabetes. Key predictive features included coding or transcript-altering SNVs, distance to gene, and open chromatin-based metrics. This work outlines a simple, understandable approach to prioritize genes at GWAS loci for functional follow-up and drug development, and provides a systematic strategy for prioritization of GWAS target genes.
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Data availability
All accession codes and URLs for publicly available data are provided in the “Methods”. Newly generated DNase-seq data are available from the GEO repository under accession GSE142160. The data that support the findings of this study are available from GitHub at https://github.com/richardslab/Ei. This includes raw data underlying the figures. Results can be visualized at http://hugeamp.org/effectorgenes.html.
Code availability
The code that supports the findings of this study are available from GitHub at https://github.com/richardslab/Ei and https://github.com/mauranolab/UKBB_FINEMAP_targetgene.
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Acknowledgements
This research has been conducted using the UK Biobank Resource using project number 27449.
Funding
The funding agencies had no role in the design, implementation or interpretation of this study. The views expressed in this article are those of the author(s) and not necessarily those of funders. MIM has received funding from the NIH: U01-DK105535 and the Wellcome Trust: Wellcome: 090532, 098381, 106130, 203141, 212259. The Greenwood lab acknowledges support from Compute Canada (RAPI: nzt-671-aa). MTM is partially funded by National Institutes of Health grant R35GM119703. The Richards research group is supported by the Canadian Institutes of Health Research (CIHR), the Lady Davis Institute of the Jewish General Hospital, the Canadian Foundation for Innovation, the NIH Foundation, Cancer Research UK and the Fonds de Recherche Québec Santé (FRQS). JBR is supported by a FRQS Clinical Research Scholarship. TwinsUK is funded by the Welcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.
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EF is an employee of Pfizer. MIM has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. MIM is currently at Genentech, 1 DNA Way, South San Francisco, CA 94080, and a holder of Roche stock. JBR has served as an advisor to GlaxoSmithKline and Deerfield Capital. JBR’s institution has received investigator-initiated grant funding from Eli Lilly, GlaxoSmithKline and Biogen for projects unrelated to this research. JBR is the CEO of 5 Prime Sciences (http://www.5primesciences.com). VF is an employee of 5 Prime Sciences.
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Forgetta, V., Jiang, L., Vulpescu, N.A. et al. An effector index to predict target genes at GWAS loci. Hum Genet 141, 1431–1447 (2022). https://doi.org/10.1007/s00439-022-02434-z
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DOI: https://doi.org/10.1007/s00439-022-02434-z